Where to Go: A Spatial Social Force Graph Neural Network for Predicting Pedestrian Trajectories From Videos With Complex Motion Scenarios

Shaojie Qiao, Rongmin Tang, Leying Pan, Haosong Gou, Nan Han, Chunfang Yang, Guan Yuan, Tao Wu, Xindong Wu

Abstract: Traditional pedestrian trajectory prediction models focus on spatio–temporal data without proper consideration of individual interactions with the environment, mutual interactions, and contextual information, resulting in low prediction performance in real applications. In this article, we propose a new pedestrian trajectory prediction model called spatial social force graph neural network (SSF-GNN). First, SSF-GNN adopts a gate recurrent unit (GRU) network and a CenterNet network to capture pedestrian trajectory features and environmental features from historical trajectory sequences. Particularly, SSF-GNN can quantify pedestrian interactions and context-awareness information based on social force. Second, SSF-GNN employs a graph neural network to integrate social influence and hidden states of pedestrians. The distance between adjacent trajectory points is approximated by the weighted average summation of pedestrian historical trajectories. Third, SSF-GNN employs a new interaction function between pedestrians by considering the distance between pedestrians, as well as the movement speed of pedestrians in the social force model, to accurately predict trajectories of pedestrians. Extensive experiments are conducted on two famous datasets, and the results demonstrate SSF-GNN’s outperforms the state-of-the-art models, where average displacement error (ADE) is reduced by more than 25.6%, and final displacement error (FDE) is reduced by more than 15.4%. When predicting a pedestrian’s trajectory in the next eight frames of locations, SSF-GNN outperforms other models significantly with an accuracy of 69.71%.

External IDs:doi:10.1109/tcss.2025.3617777